Paper Citation Record · LEDGER

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution

As of 19 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 4 inbound Pith citation observations for arXiv:2412.02960.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.02960 v1

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:57:41.117220Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

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measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T07:56:31.984721Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T20:07:21.003733Z

Reference resolution

56 of 56 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7e1ebce5-b02a-4e15-9452-ed3d95b0091f · outbound

This paper cites Semantic segmentation guided real-world super-resolution.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Semantic segmentation guided real-world super-resolution

Reference 1

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Observation 87b1f419-c217-4b43-998c-5ba5ac922e6b · outbound

This paper cites Ntire 2017 challenge on single image super-resolution: Dataset and study.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Ntire 2017 challenge on single image super-resolution: Dataset and study

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.524046Z

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Observation 7ed33e6e-2b69-4a04-b29c-a9fa55c7ce02 · outbound

This paper cites Dream- clear: High-capacity real-world image restoration with privacy-safe dataset curation.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Dream- clear: High-capacity real-world image restoration with privacy-safe dataset curation

Reference 3

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Observation ebcd9067-db4a-4df9-b9c3-3a1314c74ad8 · outbound

This paper cites Toward real-world single image super-resolution: A new benchmark and a new model.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Toward real-world single image super-resolution: A new benchmark and a new model

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.509225Z

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Observation 1cdb8981-3e51-4b32-9dd1-5dc0685d3950 · outbound

This paper cites Activating more pixels in image super- resolution transformer.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Activating more pixels in image super- resolution transformer

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.500828Z

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Observation a524d278-3a72-4841-ba4b-479df3132822 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Diffusion models beat gans on image synthesis

Reference 6

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Observation 554b5491-6bf1-42b4-ba7b-cab3f21373ae · outbound

This paper cites Image quality assessment: Unifying structure and texture similarity.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Image quality assessment: Unifying structure and texture similarity

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.485505Z

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Observation e2366c3a-2b5f-40b6-be49-4a28a0477cef · outbound

This paper cites Image super-resolution using deep convolutional net- works.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Image super-resolution using deep convolutional net- works

Reference 8

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Observation d01a9c6c-c27a-48fe-ae8a-eb2480e5642f · outbound

This paper cites Adadiffsr: Adaptive region-aware dynamic acceleration diffusion model for real-world image super-resolution.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Adadiffsr: Adaptive region-aware dynamic acceleration diffusion model for real-world image super-resolution

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.470484Z

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Observation a09e8be9-b97f-4632-af8b-ee50d9dc4325 · outbound

This paper cites Generative adversarial networks.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Generative adversarial networks

Reference 10

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Observation e36e8688-f27d-433b-975d-336175040a79 · outbound

This paper cites Div8k: Diverse 8k resolution image dataset.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Div8k: Diverse 8k resolution image dataset

Reference 11

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Observation 53e39988-a47d-468e-bace-240d37064fc8 · outbound

This paper cites Segnext: Rethinking convolutional attention design for semantic segmentation.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Segnext: Rethinking convolutional attention design for semantic segmentation

Reference 12

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Observation 88953418-e727-4547-913c-1526e6ef931e · outbound

This paper cites Denoising diffu- sion probabilistic models.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Denoising diffu- sion probabilistic models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.438185Z

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Observation 1c35f4ee-120e-437a-9c1b-3fadb6d4002e · outbound

This paper cites Argmax flows and multinomial dif- fusion: Learning categorical distributions.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Argmax flows and multinomial dif- fusion: Learning categorical distributions

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.430760Z

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Observation d5606796-48d5-4b63-a140-ff1ae402df78 · outbound

This paper cites A style-based generator architecture for generative adversarial networks.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution A style-based generator architecture for generative adversarial networks

Reference 15

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Observation 6ca94420-d7bf-4839-a805-acf7250b916a · outbound

This paper cites Imagic: Text-based real image editing with diffusion models.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Imagic: Text-based real image editing with diffusion models

Reference 16

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Observation d025b339-ae1f-405a-8a5c-e163cae93deb · outbound

This paper cites Musiq: Multi-scale image quality transformer.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Musiq: Multi-scale image quality transformer

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.414756Z

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Observation 9b31ebd3-e1e2-45a3-983a-06e81a242fd2 · outbound

This paper cites Accurate image super-resolution using very deep convolutional net- works.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Accurate image super-resolution using very deep convolutional net- works

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.405626Z

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Observation de1c80c5-8756-4cdd-a5da-5c2ed3116409 · outbound

This paper cites Segment any- thing.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Segment any- thing

Reference 19

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Observation b7eb2a15-f08c-4b48-a2f0-b859badac2ad · outbound

This paper cites Denoising Diffusion Semantic Segmentation with Mask Prior Modeling.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Denoising Diffusion Semantic Segmentation with Mask Prior Modeling

Reference 20

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Observation 16e9583d-645c-40e8-acb4-bbc0936f11cc · outbound

This paper cites Srdiff: Single image super-resolution with diffusion probabilistic models.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Srdiff: Single image super-resolution with diffusion probabilistic models

Reference 21

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Observation 47ad881e-11ac-4831-bf66-e1def88762c3 · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.389948Z

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Observation 9ba83116-df33-4fe2-be6f-f2f06b15f66f · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 23

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Observation bd1926d3-9268-44f4-9238-919046acbc53 · outbound

This paper cites Efficient and degradation-adaptive network for real-world image super- resolution.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Efficient and degradation-adaptive network for real-world image super- resolution

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.377747Z

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Observation 1adbca3c-422a-4d64-ac0d-598dea07be55 · outbound

This paper cites Efficient and degradation-adaptive network for real-world image super- resolution.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Efficient and degradation-adaptive network for real-world image super- resolution

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.369244Z

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Observation 2e374f64-4015-4249-b315-77196bc11bb5 · outbound

This paper cites DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior

Reference 26

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Observation 56ed39ba-402e-495d-913c-c3fa5d1dfe64 · outbound

This paper cites Visual instruction tuning.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Visual instruction tuning

Reference 27

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Observation 50164891-2d2c-434c-bdc2-a5af537e1811 · outbound

This paper cites Transformer for single image super-resolution.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Transformer for single image super-resolution

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.353660Z

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Observation 373413c6-afc9-4fe5-b717-0e5409e10014 · outbound

This paper cites Content-aware local gan for photo-realistic super-resolution.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Content-aware local gan for photo-realistic super-resolution

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.345924Z

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Observation 7e7f0629-8910-42e9-ac09-5fab14f98221 · outbound

This paper cites Spire: Semantic prompt-driven image restoration.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Spire: Semantic prompt-driven image restoration

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.337738Z

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Observation 8a514025-939f-4b96-a857-7dc0bfcb7523 · outbound

This paper cites Xpsr: Cross-modal priors for diffusion-based image super-resolution.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Xpsr: Cross-modal priors for diffusion-based image super-resolution

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.329785Z

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Observation 0c12e969-7287-4d45-9534-69202c7b7a72 · outbound

This paper cites High-resolution image syn- thesis with latent diffusion models.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution High-resolution image syn- thesis with latent diffusion models

Reference 32

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Observation d1b7f669-843f-4fa8-8bac-2d895370f9b5 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution U-net: Convolutional networks for biomedical image segmentation

Reference 33

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Observation 1a8f6ab4-bdc7-4f9e-b0b8-9628ab9261ca · outbound

This paper cites Image super- resolution via iterative refinement.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Image super- resolution via iterative refinement

Reference 34

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Observation 984cfb81-e5c7-45de-b707-36b1c4318e33 · outbound

This paper cites Denoising Diffusion Implicit Models.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Denoising Diffusion Implicit Models

Reference 35

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Observation 697dfbd1-5b64-4260-8de4-bcad18f5cd53 · outbound

This paper cites Ntire 2017 challenge on single image super-resolution: Methods and results.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Ntire 2017 challenge on single image super-resolution: Methods and results

Reference 36

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Observation 288bbb97-41f7-42d2-bf24-8d4216f50f38 · outbound

This paper cites SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolution.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolution

Reference 37

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Observation 7c008707-0e34-406a-9c1b-5953adf36f64 · outbound

This paper cites Ex- ploring clip for assessing the look and feel of images.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Ex- ploring clip for assessing the look and feel of images

Reference 38

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Observation 4f4e7ba7-b730-4104-baf4-628966acdb26 · outbound

This paper cites Exploiting diffusion prior for real-world image super-resolution.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Exploiting diffusion prior for real-world image super-resolution

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.288820Z

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Observation 028f7eb4-a244-4587-8ba1-fd11b0d65ec3 · outbound

This paper cites Recovering realistic texture in image super-resolution by deep spatial feature transform.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Recovering realistic texture in image super-resolution by deep spatial feature transform

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.281163Z

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Observation b634c088-b503-470f-a185-74904c03d53e · outbound

This paper cites Real-esrgan: Training real-world blind super-resolution with pure synthetic data.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Real-esrgan: Training real-world blind super-resolution with pure synthetic data

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.273332Z

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Observation 6283b7f0-87f9-4959-8be1-4f5bae18d337 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Image quality assessment: from error visibility to structural similarity

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.266076Z

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Observation 70291548-1ff4-4f0e-b0cf-fa8e9019cc5b · outbound

This paper cites Component divide-and-conquer for real-world image super-resolution.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Component divide-and-conquer for real-world image super-resolution

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.258174Z

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Observation 255ca58e-3429-4e3d-85b3-28cfe01e5a2f · outbound

This paper cites Seesr: Towards semantics-aware real-world image super-resolution.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Seesr: Towards semantics-aware real-world image super-resolution

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.250840Z

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Observation 999afb6b-aa20-4bb8-ac83-9dfad26b13dc · outbound

This paper cites Segformer: Simple and ef- ficient design for semantic segmentation with transformers.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Segformer: Simple and ef- ficient design for semantic segmentation with transformers

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.243362Z

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Observation 7e4e9cc3-75d5-4332-8008-616579d486a4 · outbound

This paper cites Maniqa: Multi-dimension attention network for no-reference image quality assessment.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Maniqa: Multi-dimension attention network for no-reference image quality assessment

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.235349Z

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Observation 5785b554-bd2a-4bd9-a0a7-52ca3eb86e2f · outbound

This paper cites Pixel-aware stable diffusion for realistic image super-resolution and personalized stylization.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Pixel-aware stable diffusion for realistic image super-resolution and personalized stylization

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.227867Z

Source-reported events for the cited work

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Observation 70f730c8-960d-4e4a-ab3b-01699c7f7a76 · outbound

This paper cites Scaling up to excellence: Practicing model scaling for photo- realistic image restoration in the wild.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Scaling up to excellence: Practicing model scaling for photo- realistic image restoration in the wild

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.220086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8e774914-802d-46f9-9d35-9d08b945b3f4 · outbound

This paper cites Resshift: Efficient diffusion model for image super- resolution by residual shifting.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Resshift: Efficient diffusion model for image super- resolution by residual shifting

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.212198Z

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Observation 0b46bfb4-832e-4ad5-9515-e17d0dea0fd5 · outbound

This paper cites Designing a practical degradation model for deep blind image super-resolution.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Designing a practical degradation model for deep blind image super-resolution

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.203841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation baa612fa-1ba7-4a14-b50a-581e8baf8a52 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Adding conditional control to text-to-image diffusion models

Reference 51

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Observation 1c8b61b6-0756-4782-bafd-be16d633d5a7 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution The unreasonable effectiveness of deep features as a perceptual metric

Reference 52

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Observation 3f1337d4-e3e5-408f-a858-b835ca5d3507 · outbound

This paper cites Efficient long-range attention network for image super- resolution.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Efficient long-range attention network for image super- resolution

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.187330Z

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Observation ff178bb6-d800-4561-840b-cf703d2b3cff · outbound

This paper cites Recognize anything: A strong image tagging model.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Recognize anything: A strong image tagging model

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.179482Z

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Observation 6f9c88f0-2d85-4ace-af92-f041d02884e8 · outbound

This paper cites Generalized decoding for pixel, image, and language.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Generalized decoding for pixel, image, and language

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.171769Z

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Observation 3d1a3599-bc44-42ac-9956-f8c6f4060694 · outbound

This paper cites Segment everything everywhere all at once.

Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution Segment everything everywhere all at once

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:57:41.163006Z

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Pith citing papers

Observation 6cfed5c0-a4c0-4ce9-8dcc-90412f02525c · inbound

Semantic-Guided Cross-Sensor Super Resolution of Remote Sensing Images: A Gated Dual Conditioning Flow Matching Model cites this paper.

Semantic-Guided Cross-Sensor Super Resolution of Remote Sensing Images: A Gated Dual Conditioning Flow Matching Model Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:31.984721Z

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Observation 3d53b273-48b7-4dab-a446-2e19f3e0494e · inbound

Adaptive Context Matters: Towards Provable Multi-Modality Guidance for Super-Resolution cites this paper.

Adaptive Context Matters: Towards Provable Multi-Modality Guidance for Super-Resolution Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:41:24.894012Z

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Observation 4e20ec40-bbc3-40c3-8f09-1b6132887fcc · inbound

Joint Medical Image Enhancement and Segmentation with Diffusion-based Symbiotic Information Interaction cites this paper.

Joint Medical Image Enhancement and Segmentation with Diffusion-based Symbiotic Information Interaction Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-02T20:07:21.005909Z

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Observation fbffc552-d3d1-4d17-a0f3-bd8c848f1f01 · inbound

MicroZoom: Structure-Preserving Detail Synthesis at Extreme Scale cites this paper.

MicroZoom: Structure-Preserving Detail Synthesis at Extreme Scale Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution

Reference 41

Resolution: unresolved; no resolver link, observed 2026-07-31T06:40:45.601714Z

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